Eliminate the "plastic" AI look — now on Krea 2. This is the first ever port of the Realistic Snapshot series (100K+ downloads on Z-Image-Turbo) to a brand-new base model. It's engineered to inject raw, organic texture and lifelike lighting physics into your generations, mimicking the imperfections that make a photo feel real — from intricate skin details (visible pores, vellus hair) to the distinct characteristics of digital sensors (ISO grain, flash bloom).

✨ About this version (Experimental)

Retrained from the ground up on a 600-image dataset — double the size of the v5 Z-Image-Turbo set. The new data leans heavily on modern iPhone photography with a much larger NSFW spread, replacing the early-2000s amateur shots that weighed down the older datasets. The result is the same signature Realistic Snapshot style, just cleaner, more versatile, and better balanced.

How Krea 2 compares to Z-Image-Turbo:

Prompt adherence is significantly better — Krea 2 actually listens
Z-Image still edges it out on raw style and realism (for now)
This version excels at enhancing and boosting detail in images — check the showcase
It behaves very differently: old prompts and workflows will not carry over

⚠️ Experimental status: Krea 2 is a brand-new model and the prompting conventions around it aren't established yet — results can range a lot between generations. I'm actively refining this and sharing settings as I lock them down. Feedback on what works and what doesn't is hugely appreciated (drop it in the comments or Discord).

💡 Usage Tips

Recommended Strength: 1.0 – 1.5, sometimes even 2.0 depending on the image. Krea 2 takes the LoRA very differently than Z-Image — don't be afraid to push past the usual range.
Prompting: Krea 2 uses dense natural-language captions, so describe the photographic qualities you want in full sentences rather than tag lists. Phrases worth working in: casual smartphone snapshot, candid, direct on-camera flash, high ISO noise, visible pores, detailed skin texture, wide-angle distortion, shallow depth of field.
Experiment. Seriously — the model rewards it, and I want to see what you find.